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Gait is a unique biometric feature that can provide reliable information to recognize emotions even when viewed from a distance. However, the insufficient amount and diversity of training data annotated with emotions severely hinder the application of gait emotion recognition. In this paper, we propose an adversarial learning framework for emotional gait dataset augmentation, with which a two-stage model can be trained to generate a number of synthetic emotional samples by separating identity and emotion representations from gait trajectories. To our knowledge, this is the first work to realize the mutual transformation between natural gait and emotional gait. Experimental results reveal that the synthetic gait samples generated by the proposed networks are rich in emotional information. As a result, the emotion classifier trained on the augmented dataset is competitive with state-of-the-art gait emotion recognition works.<\/jats:p>","DOI":"10.1017\/s0263574722001813","type":"journal-article","created":{"date-parts":[[2023,2,6]],"date-time":"2023-02-06T14:18:17Z","timestamp":1675693097000},"page":"1452-1465","source":"Crossref","is-referenced-by-count":6,"title":["Data augmentation by separating identity and emotion representations for emotional gait recognition"],"prefix":"10.1017","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4498-8351","authenticated-orcid":false,"given":"Weijie","family":"Sheng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoyan","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinde","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"56","published-online":{"date-parts":[[2023,2,6]]},"reference":[{"key":"S0263574722001813_ref4","doi-asserted-by":"publisher","DOI":"10.1017\/S0263574716000722"},{"key":"S0263574722001813_ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.107868"},{"key":"S0263574722001813_ref19","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1109\/TAFFC.2017.2684799","article-title":"Cross-corpus acoustic emotion recognition with multi-task learning: seeking common ground while preserving differences","volume":"10","author":"Zhang","year":"2019","journal-title":"IEEE Trans. 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